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CHANCERY: Evaluating Corporate Governance Reasoning Capabilities in Language Models

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read CHANCERY, a new benchmark of 502 questions pairing real corporate charters with proposed board or shareholder actions, shows that state-of-the-art language models and tool-using agents still fall short of reliable legal reasoning, with…

desk verdict A useful new benchmark whose gold-standard labels need external validation before the accuracy numbers can be trusted. read the letter →

arxiv 2506.04636 v2 pith:B6UYJJQU submitted 2025-06-05 cs.AI cs.CL

Lucas Irwin , Arda Kaz , Peiyao Sheng , Sewoong Oh , Pramod Viswanath This is my paper
classification cs.AIcs.CL
keywords corporategovernancelegalreasoninglanguagemodelsbenchmarkbinaryclassificationcharteranalysisDelawarelawagents
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces CHANCERY, a benchmark that asks a language model whether a proposed executive, board, or shareholder action is consistent with a real company's corporate-governance charter and Delaware law. The benchmark contains 502 handcrafted yes/no questions over 79 real charters, built around 24 governance principles from a well-known study of 10,000 charters. The paper claims this is the first benchmark aimed specifically at legal reasoning about corporate governance, as opposed to classification or entailment over legal texts. Its central finding is that current state-of-the-art models struggle with the task: Claude 3.7 Sonnet reaches 64.5% accuracy, GPT-4o 75.2%, and the best system tested, a tool-using agent, only 78.1%. The authors read this as evidence that corporate-governance reasoning remains an open challenge for even advanced reasoning systems, and they break down the failures by governance principle, showing that anti-greenmail, secret-ballot, and poison-pill cases are the hardest.

What carries the argument

The load-bearing object is the benchmark itself: a pair consisting of a corporate charter and a proposed action, fed into a model that must return a single yes or no. CHANCERY is built from 24 corporate-governance principles drawn from a study of 10,000 real charters, 79 of those charters chosen across industries, and 502 handcrafted hypothetical proposals written with assistance from a large language model. Gold labels come from the authors' own reasoning, written out as explanation paragraphs; the task is deliberately multi-step, mixing charter clauses with Delaware General Corporation Law so that a model must synthesize several lines of text. The machinery that carries the evaluation beyond single models is a pair of tool-using agents that can search for external legal information before answering, and it is these agents that achieve the highest reported accuracy.

What would settle it

Have a panel of independent corporate-law attorneys answer a random sample of the 502 questions without seeing the authors' labels; if agreement between the attorneys and the gold labels falls well below the accuracy floor claimed for the best models (roughly 75-78% agreement, or even below the disagreement rate among attorneys), the benchmark's labels are too subjective to support the paper's conclusions. A second check: fine-tune a small language model on the explanation paragraphs alone without the charter text; if it then predicts the authors' labels at near-perfect accuracy, the benchmark may be measuring label style rather than legal reasoning.

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Extended reading notes

Core claim

On the paper's own terms, the central claim is that CHANCERY validly measures a type of legal reasoning that existing benchmarks miss: deciding whether a proposed action is compatible with the governing charter and with Delaware corporate law, often by combining several charter clauses and outside legal principles. The authors construct the benchmark by selecting 79 real charters, framing each handcrafted hypothetical around one of 24 governance principles, and labeling each question by their own reading of the charter and the law. They report that no model comes close to reliable performance, with plain reasoning models scoring between 55.4% and 75.2% and tool-using agents reaching 76.1% and 78.1%. A breakdown of DeepSeek-R1's answers shows accuracy falling on multi-hop questions (58.1% vs 73.3% single-hop) and on questions needing an external legal search (58.0% vs 70.7%), and especially low accuracy on anti-greenmail (27.6%), secret ballots (33.3%), and poison pills (41.9%). The authors take these results to show that legal reasoning requires both domain alignment and multi-step deduction, and they offer the benchmark as a reusable test for future systems.

Load-bearing premise

The gold-standard answers are the authors' own determinations of what the charter and Delaware law require, reported without independent legal review or inter-annotator agreement, so if those determinations are wrong or idiosyncratic, the accuracy figures do not measure legal reasoning.

Editorial extensions

If this is right

  • Any model claiming strong legal reasoning ability should be expected to score well above 78.1% on CHANCERY, since that is the current best agent result.
  • Performance varies sharply by governance principle, so aggregate accuracy hides the fact that the hardest categories — anti-greenmail, secret ballots, and poison pills — remain far from solved.
  • Multi-hop questions and questions requiring external legal cross-referencing are systematically harder than single-hop, self-contained questions.
  • Adding tool use and external search improves accuracy over plain prompting, but not enough to make the task reliable.
  • The benchmark supplies a reusable test for future legal reasoning systems, including custom-built legal agents.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension would be to test retrieval-augmented legal systems that pull up relevant statutes or precedents on demand, since the paper's result that search-capable agents outperform plain models implies such systems may close much of the remaining gap.
  • A robustness check the paper leaves implicit is whether the 24-principle taxonomy and the charter set fully cover corporate-governance law, or whether the benchmark's difficulty partly reflects question phrasing; rewriting the same questions on the same charters would separate those factors.
  • The principle-level accuracy table invites a diagnostic use: developers could train or fine-tune models specifically on the weakest principles (anti-greenmail, secret ballots, poison pills) and measure whether CHANCERY scores rise, giving a concrete benchmark-driven curriculum.
  • Because all 79 charters are US-based, the benchmark's generalizability to other jurisdictions is untested; extending it to non-US charts could reveal whether the reasoning deficit is about law in general or about the specific Delaware-law context.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper introduces CHANCERY, a binary classification benchmark for corporate-governance reasoning in language models. The benchmark consists of 502 questions, each pairing a hypothetical executive/board/shareholder action with one of 79 real corporate charters drawn from a dataset of 10,000 charters, guided by 24 governance principles from Gompers et al. (2003). The task is to determine whether the proposed action is consistent with the charter and Delaware law. The authors construct the questions with human iteration assisted by GPT-4o, and the gold-standard answers are decided by the authors themselves, with a written explanation for each. Evaluations of several LLMs and two agent frameworks (ReAct and CodeAct) show accuracies ranging from 55.4% (QwQ-32B) to 78.1% (CodeAct agent). The paper also reports an analysis of DeepSeek-R1's performance across governance principles, reasoning complexity, and the need for external search. The benchmark and evaluation code are released via an anonymous repository.

Significance. If the gold-standard labels are accepted as valid, CHANCERY fills a genuine gap: it is one of the few legal benchmarks that targets multi-step reasoning over real corporate governance texts combined with external legal doctrine, rather than classification or entailment over isolated clauses. The release of the dataset and code is a valuable contribution, and the finding that current state-of-the-art models, including reasoning models, achieve only modest accuracy on a binary decision task is a useful result for the community. The analysis of which principles and reasoning patterns are hardest is informative, though it is based on a single model. The main weakness is that the validity of the benchmark hinges on the authors' self-authored gold labels, which are not externally validated; this limits the strength of any claim that the benchmark measures legal reasoning rather than agreement with the authors' private interpretation.

major comments (4)
  1. [§3.3, step 4] The gold-standard labels are the authors' own determinations, with the only validation being the statement that 'we did not find any discrepancy among the decisions' of the different humans working on the dataset. No inter-annotator agreement measure, external legal review, or detailed scoring rubric is reported. This is load-bearing because the central claim that CHANCERY measures legal reasoning rests entirely on the correctness of the 502 binary labels. The Appendix A example for Red Robin Gourmet Burgers illustrates the issue: the correct answer 'No' is justified by the view that 'charter silence does not confer a blank check,' while a contrary reading relying on board discretion under Article FIFTH is also defensible. Without independent audit or at least a quantitative measure of label reliability, the reported accuracies (e.g., 75.2% for GPT-4o, 78.1% for CodeAct) may simply measure agreement with the authors' interpretation. I recommend an external legal-expert audit of a random sample of items, report of inter-annotator agreement on a subset, or provision of a detailed rubric that allows replication of label decisions.
  2. [§4.1, Table 2] The paper states that without the specialized binary-output prompt, GPT-4o and Llama3.3-70B accuracies drop to 29.1% and 22.1%, respectively. These numbers are mentioned only in the text and are not included in Table 2, which reports only the strict-prompt results. This extreme prompt sensitivity suggests that the benchmark may measure instruction-following and output-format robustness as much as legal reasoning, and it raises questions about the stability of the model ranking. The authors should present the default-prompt results prominently (e.g., in Table 2 or a separate table), discuss why the strict prompt is the appropriate evaluation protocol, and ideally report results from at least two different instruction formats to demonstrate that relative rankings are not an artifact of prompt choice.
  3. [§3.3 and §4.2] The paper repeatedly emphasizes that questions require external knowledge, such as the Delaware General Corporation Law and case law (e.g., Unocal, Blasius), and that some questions require an 'additional search.' However, the benchmark as released does not appear to include the external sources, and the evaluation protocol does not specify whether models (including the agents) are given access to such sources or how a search is performed. The DeepSeek-R1 analysis (Section 4.2) reports an accuracy difference between questions that require search (58.0%) and those that do not (70.7%), but the assignment of questions to these categories is not described, and the agent evaluation does not document what tools or corpora the agents used. This is load-bearing for interpreting the results as an assessment of reasoning over the charter rather than of the model's memorized legal knowledge. The authors should clarify what external information is required for each question, make that information available to the evaluator, and specify the search procedure (if any) used in the agent runs.
  4. [§4.1, Table 2] No error bars or repeated runs are reported for any of the model accuracies. For a binary task with 502 items, the standard error of an accuracy estimate near 78% is approximately 1.8 percentage points, meaning the difference between GPT-4o (75.2%) and CodeAct (78.1%) is not clearly significant. The paper's claims that reasoning agents 'exhibit superior performance' and that models 'struggle' on the benchmark would be strengthened by confidence intervals, bootstrap estimates, or multiple independent runs with different random seeds (especially for the agents, which may be nondeterministic). Without such information, the relative ordering of models is not well supported.
minor comments (6)
  1. [§4.1, Table 2] The model name is inconsistent: the text in Section 4.1 refers to 'Llama3.3-70B' while Table 2 lists 'Llama3.1-70B.' Please clarify which model was actually evaluated.
  2. [Appendix A] In the Red Robin example, the correct answer is 'No' and the DeepSeek-R1 final answer is shown as 'YES.' However, the accompanying explanation states that 'the model incorrectly concluded that the severance plan was invalid,' which would correspond to an answer of 'No.' This contradiction makes the error analysis in that example difficult to follow and should be corrected.
  3. [Abstract and §1] The text refers to 'the seminal work of [6]' and 'established in [6]' but the reference number is used without the author names; consider spelling out 'Gompers et al. (2003)' on first mention for readability.
  4. [§5, Discussion] The word 'conspicuosly' is a typo for 'conspicuously.'
  5. [Figure 1(b) and §3.2] The industry distribution lists 'Other (16)' as the largest category, but the text does not explain what companies fall into this bucket. Elaborating on the composition of 'Other' would improve transparency about benchmark diversity.
  6. [§4.2] The reference 'The example displayed in 2' should be 'Figure 2' for clarity, and the caption for Table 3 should be consistent about the hyphenation of 'Anti-greenmail.'

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: CHANCERY's labels are human judgments independent of the evaluated models, and the only self-citation (Open Deep Search agents) is not load-bearing.

full rationale

The paper's construction chain is: select 24 governance principles from Gompers et al. [6], sample 79 real charters, handcraft 502 hypothetical proposals, and label them by human judgment (Section 3.3). There is no equation, fitted parameter, or statistical model connecting these inputs to the reported accuracies; the evaluations are held-out measurements against fixed binary labels. The gold-standard labels were set by the authors ('the decision carefully made by a human (one of us)'), not derived from any model output, so no 'prediction' is forced by construction. The use of GPT-4o to help invent scenarios while GPT-4o is later evaluated raises a benchmark-validity concern (possible style familiarity), but it is not a circular reduction: the model never generates the labels. The only self-citation is reference [1] (Open Deep Search, which includes several of the present authors) used to motivate the ReAct and CodeAct agents in Section 4.1. This is not load-bearing for the paper's central claim that CHANCERY is difficult: the agent scores are supporting results, and the underlying frameworks are also attributed to independent prior work [12, 21]. The acknowledged limitation in Section 3.3 step 4—no inter-annotator agreement or external legal review—is a correctness/validity risk, not circularity; it does not make the derivation equivalent to its inputs. Under the stated rubric, the appropriate finding is no significant circularity (score 0-2), with a score of 1 reflecting the minor, non-load-bearing self-citation.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim rests on (1) the Gompers et al. taxonomy of 24 governance principles, (2) the correctness of the authors' hand labels, and (3) the representativeness of the 79 charters. No free parameters or invented entities are introduced; the benchmark is a labeled dataset.

assumptions (3)
  • domain assumption The 24 governance principles from Gompers et al. (2003) are a comprehensive and correct taxonomy for corporate governance provisions.
    The benchmark is built on these principles (Section 3.3). If the taxonomy is incomplete or misapplied, the benchmark coverage claim fails.
  • domain assumption The authors' hand-labeled ground truth answers are the correct legal outcomes.
    Section 3.3, step 4. No inter-annotator agreement or external legal review is reported.
  • ad hoc to paper The 79 selected charters are representative of the 10k dataset and allow the constructed questions to be answered from the charter text and general law.
    Section 3.3, step 1. The selection is described as random, but no distribution analysis is provided.

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Cite this review

Pith. "Pith review of CHANCERY: Evaluating Corporate Governance Reasoning Capabilities in Language Models." pith.science (2026). https://pith.science/paper/B6UYJJQU

@misc{pith2026250604636,
  author       = {Pith},
  title        = {Pith review of: CHANCERY: Evaluating Corporate Governance Reasoning Capabilities in Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B6UYJJQU}},
  note         = {Machine review of arXiv:2506.04636}
}
read the original abstract

Law has long been a domain that has been popular in natural language processing (NLP) applications. Reasoning (ratiocination and the ability to make connections to precedent) is a core part of the practice of the law in the real world. Nevertheless, while multiple legal datasets exist, none have thus far focused specifically on reasoning tasks. We focus on a specific aspect of the legal landscape by introducing a corporate governance reasoning benchmark (CHANCERY) to test a model's ability to reason about whether executive/board/shareholder's proposed actions are consistent with corporate governance charters. This benchmark introduces a first-of-its-kind corporate governance reasoning test for language models - modeled after real world corporate governance law. The benchmark consists of a corporate charter (a set of governing covenants) and a proposal for executive action. The model's task is one of binary classification: reason about whether the action is consistent with the rules contained within the charter. We create the benchmark following established principles of corporate governance - 24 concrete corporate governance principles established in and 79 real life corporate charters selected to represent diverse industries from a total dataset of 10k real life corporate charters. Evaluations on state-of-the-art (SOTA) reasoning models confirm the difficulty of the benchmark, with models such as Claude 3.7 Sonnet and GPT-4o achieving 64.5% and 75.2% accuracy respectively. Reasoning agents exhibit superior performance, with agents based on the ReAct and CodeAct frameworks scoring 76.1% and 78.1% respectively, further confirming the advanced legal reasoning capabilities required to score highly on the benchmark. We also conduct an analysis of the types of questions which current reasoning models struggle on, revealing insights into the legal reasoning capabilities of SOTA models.

Figures

Figures reproduced from arXiv: 2506.04636 by the authors.

Figure 1
Figure 1. CHANCERY Benchmark example and industry distribution. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. An example question and DeepSeek-R1 answer from the CHANCERY benchmark based [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DECODEM: Data Extraction from Corporate Organizational Documents via Enhanced Methods

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Frontier LLMs accurately extract many corporate governance variables from charters and bylaws on the new DECODEM benchmarks, but complex provisions and label noise remain the main error sources.

Reference graph

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